Using gamification to increase map data production during humanitarian volunteered geographic information (VGI) campaigns

Watkinson, Kirsty; Huck, Jonny; Harris, Angela

doi:10.1080/15230406.2022.2156389

Cited by 4 publications

(1 citation statement)

References 48 publications

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“…An example of an application aimed at the area of Augmented Reality, regardless of its context, is described by Ogi et al It addresses the problem of the need for an AR marker or location information by replacing it with a machine-learning system fed with learning data automatically collected from the users with the help of gamification [27]. Map Safari is an example of an application in the area of Geographic Information Systems, which proposes a machine-learning-supported workflow making use of gamification to encourage volunteers to sustain the contribution of map data for humanitarian organizations without degrading their quality [28].…”

Section: Areas Of Applicationmentioning

confidence: 99%

Machine Learning in Gamification and Gamification in Machine Learning: A Systematic Literature Mapping

Swacha,

Gracel

2023

Applied Sciences

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Albeit in different ways, both machine learning and gamification have transfigured the user experience of information systems. Although both are hot research topics, so far, little attention has been paid to how these two technologies converge with each other. This relation is not obvious as while it is feasible to enhance gamification with machine learning, it is also feasible to support machine learning with gamification; moreover, there are applications in which machine learning and gamification are combined yet not directly connected. In this study, we aim to shed light on the use of both machine learning in gamification and gamification in machine learning, as well as the related topics of using gamification in machine learning education and machine learning in gamification research. By performing a systematic literature mapping, we not only identify prior works addressing these respective themes, but also analyze how their popularity evolved in time, investigate the areas of application reported by prior works, used machine learning techniques and software tools, as well as the character of research contribution and the character of evaluation results for works that presented them.

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Section: Areas Of Applicationmentioning

confidence: 99%